Privacy preserving strategies for electronic health records in the era of large language models
Electronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like priva...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01429-0 |
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| _version_ | 1832594423312023552 |
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| author | Jitendra Jonnagaddala Zoie Shui-Yee Wong |
| author_facet | Jitendra Jonnagaddala Zoie Shui-Yee Wong |
| author_sort | Jitendra Jonnagaddala |
| collection | DOAJ |
| description | Electronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like privacy-preserving locally deployed LLMs, synthetic data generation, differential privacy, and deidentification. Depending on the task, strategies should be employed to increase compliance with patient privacy regulatory frameworks. |
| format | Article |
| id | doaj-art-8ca461563dfb423e9f1f04ade8ff58f6 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-8ca461563dfb423e9f1f04ade8ff58f62025-01-19T12:39:43ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811310.1038/s41746-025-01429-0Privacy preserving strategies for electronic health records in the era of large language modelsJitendra Jonnagaddala0Zoie Shui-Yee Wong1School of Population Health, UNSW SydneyGraduate School of Public Health, St. Luke’s International UniversityElectronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like privacy-preserving locally deployed LLMs, synthetic data generation, differential privacy, and deidentification. Depending on the task, strategies should be employed to increase compliance with patient privacy regulatory frameworks.https://doi.org/10.1038/s41746-025-01429-0 |
| spellingShingle | Jitendra Jonnagaddala Zoie Shui-Yee Wong Privacy preserving strategies for electronic health records in the era of large language models npj Digital Medicine |
| title | Privacy preserving strategies for electronic health records in the era of large language models |
| title_full | Privacy preserving strategies for electronic health records in the era of large language models |
| title_fullStr | Privacy preserving strategies for electronic health records in the era of large language models |
| title_full_unstemmed | Privacy preserving strategies for electronic health records in the era of large language models |
| title_short | Privacy preserving strategies for electronic health records in the era of large language models |
| title_sort | privacy preserving strategies for electronic health records in the era of large language models |
| url | https://doi.org/10.1038/s41746-025-01429-0 |
| work_keys_str_mv | AT jitendrajonnagaddala privacypreservingstrategiesforelectronichealthrecordsintheeraoflargelanguagemodels AT zoieshuiyeewong privacypreservingstrategiesforelectronichealthrecordsintheeraoflargelanguagemodels |